摘要
通过分析天津市2017年1月1日至2018年4月20日空气质量6项指标SO_2、PM2.5、PM10、O_3、CO、NO_2逐日数据,应用时间序列分析法建立ARIMA(p,d,q)(P,D,Q)模型。利用SPSS软件预测2018年4月21—27日各指标数据,将预测结果和已有数据进行对比,拟合效果很好,认为该模型预测结果较理想。再用其预测未来7 d各项空气指标数据,观察数据的变化趋势,分析原因并提出可行性解决方案。
By analyzing the daily data of six air quality indexes （S02、PM2.5、 PM10、O3、CO、NO2）in Tianjin from Jan- uary lst,2017 to April 20th,2018 ,the ARIMA （p,d,q） （P,D,Q） model was established by using the method of time series analysis.And by using SPSS Soft- ware, the model was used to predict the data of every air quality index in Tianjin from April 21st,2018 to April 27th, 2018.Comparing the prediction results with the existing data, the fitting effect was very good. It is considered that the prediction results of this model were ideal, and it would be used to predict the data of air quality index in the next seven days, to observe the trend of data and analyze the reasons and propose a feasible solution.
作者
孟庆云
张若晴
袁朱红
李智坤
冀德刚
MENG Qing-yun(Agricultural University of Hebei Science College,Baoding,Hebei 071000)
出处
《农业灾害研究》
2018年第5期44-45,共2页
Journal of Agricultural Catastrophology
基金
河北省高等学校科学技术青年基金研究项目(QN2016243)
